Aki Vehtari, Associate Professor in Computational Science, Aalto University

On Priors and Bayesian Variable Selection in Large p, Small n Regression

Abstract: The Bayesian approach is well known for using priors to improve inference, but equally important part is the integration over the uncertainties. I first present recent development in hierarchical shrinkage priors for presenting sparsity assumptions in covariate effects. I then present a projection predictive variable selection approach, which is a Bayesian decision theoretical approach for variable selection which can preserve the essential information and uncertainties related to all variables in the study. I also present recent excellent experimental results and easy to use software.

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Next talks:

May 29, Otaniemi: Jaakko Lehtinen, Graphics Meets Vision Meets Machine Learning
June 5, Kumpula: Jarno Vanhatalo
June 12, Otaniemi: Zhirong Yang: Learning Data Representation by Large-Scale Neighbor Embedding
--after June 12, we'll have a summer break and continue on September 4, 2017--

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